Recurrent epimutations activate gene body promoters in primary glioblastoma.

Aberrant DNA hypomethylation may play an important role in the growth rate of glioblastoma (GBM), but the functional impact on transcription remains poorly understood. We assayed the GBM methylome with MeDIP-seq and MRE-seq, adjusting for copy number differences, in a small set of non-glioma CpG island methylator phenotype (non-G-CIMP) primary tumors. Recurrent hypomethylated loci were enriched within a region of chromosome 5p15 that is specified as a cancer amplicon and also encompasses TERT, encoding telomerase reverse transcriptase, which plays a critical role in tumorigenesis. Overall, 76 gene body promoters were recurrently hypomethylated, including TERT and the oncogenes GLI3 and TP73. Recurring hypomethylation also affected previously unannotated alternative promoters, and luciferase reporter assays for three of four of these promoters confirmed strong promoter activity in GBM cells. Histone H3 lysine 4 trimethylation (H3K4me3) ChIP-seq on tissue from the GBMs uncovered peaks that coincide precisely with tumor-specific decrease of DNA methylation at 200 loci, 133 of which are in gene bodies. Detailed investigation of TP73 and TERT gene body hypomethylation demonstrated increased expression of corresponding alternate transcripts, which in TP73 encodes a truncated p73 protein with oncogenic function and in TERT encodes a putative reverse transcriptase-null protein. Our findings suggest that recurring gene body promoter hypomethylation events, along with histone H3K4 trimethylation, alter the transcriptional landscape of GBM through the activation of a limited number of normally silenced promoters within gene bodies, in at least one case leading to expression of an oncogenic protein.

Conserved role of intragenic DNA methylation in regulating alternative promoters.

Although it is known that the methylation of DNA in 5' promoters suppresses gene expression, the role of DNA methylation in gene bodies is unclear. In mammals, tissue- and cell type-specific methylation is present in a small percentage of 5' CpG island (CGI) promoters, whereas a far greater proportion occurs across gene bodies, coinciding with highly conserved sequences. Tissue-specific intragenic methylation might reduce, or, paradoxically, enhance transcription elongation efficiency. Capped analysis of gene expression (CAGE) experiments also indicate that transcription commonly initiates within and between genes. To investigate the role of intragenic methylation, we generated a map of DNA methylation from the human brain encompassing 24.7 million of the 28 million CpG sites. From the dense, high-resolution coverage of CpG islands, the majority of methylated CpG islands were shown to be in intragenic and intergenic regions, whereas less than 3% of CpG islands in 5' promoters were methylated. The CpG islands in all three locations overlapped with RNA markers of transcription initiation, and unmethylated CpG islands also overlapped significantly with trimethylation of H3K4, a histone modification enriched at promoters. The general and CpG-island-specific patterns of methylation are conserved in mouse tissues. An in-depth investigation of the human SHANK3 locus and its mouse homologue demonstrated that this tissue-specific DNA methylation regulates intragenic promoter activity in vitro and in vivo. These methylation-regulated, alternative transcripts are expressed in a tissue- and cell type-specific manner, and are expressed differentially within a single cell type from distinct brain regions. These results support a major role for intragenic methylation in regulating cell context-specific alternative promoters in gene bodies.

Evolution of DNA repair defects during malignant progression of low-grade gliomas after temozolomide treatment.

Temozolomide (TMZ) increases the overall survival of patients with glioblastoma (GBM), but its role in the clinical management of diffuse low-grade gliomas (LGG) is still being defined. DNA hypermethylation of the O (6) -methylguanine-DNA methyltransferase (MGMT) promoter is associated with an improved response to TMZ treatment, while inactivation of the DNA mismatch repair (MMR) pathway is associated with therapeutic resistance and TMZ-induced mutagenesis. We previously demonstrated that TMZ treatment of LGG induces driver mutations in the RB and AKT-mTOR pathways, which may drive malignant progression to secondary GBM. To better understand the mechanisms underlying TMZ-induced mutagenesis and malignant progression, we explored the evolution of MGMT methylation and genetic alterations affecting MMR genes in a cohort of 34 treatment-naïve LGGs and their recurrences. Recurrences with TMZ-associated hypermutation had increased MGMT methylation compared to their untreated initial tumors and higher overall MGMT methylation compared to TMZ-treated non-hypermutated recurrences. A TMZ-associated mutation in one or more MMR genes was observed in five out of six TMZ-treated hypermutated recurrences. In two cases, pre-existing heterozygous deletions encompassing MGMT, or an MMR gene, were followed by TMZ-associated mutations in one of the genes of interest. These results suggest that tumor cells with methylated MGMT may undergo positive selection during TMZ treatment in the context of MMR deficiency.

Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections.

Tumors may contain billions of cells including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that is consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections.

Tumors may contain billions of cells, including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that are consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project.

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Characterizing advanced breast cancer heterogeneity and treatment resistance through serial biopsies and comprehensive analytics.

Molecular heterogeneity in metastatic breast cancer presents multiple clinical challenges in accurately characterizing and treating the disease. Current diagnostic approaches offer limited ability to assess heterogeneity that exists among multiple metastatic lesions throughout the treatment course. We developed a precision oncology platform that combines serial biopsies, multi-omic analysis, longitudinal patient monitoring, and molecular tumor boards, with the goal of improving cancer management through enhanced understanding of the entire cancer ecosystem within each patient. We describe this integrative approach using comprehensive analytics generated from serial-biopsied lesions in a metastatic breast cancer patient. The serial biopsies identified remarkable heterogeneity among metastatic lesions that presented clinically as discordance in receptor status and genomic alterations with mixed treatment response. Based on our study, we highlight clinical scenarios, such as rapid progression or mixed response, that indicate consideration for repeat biopsies to evaluate intermetastatic heterogeneity (IMH), with the objective of refining targeted therapy. We present a framework for understanding the clinical significance of heterogeneity in breast cancer between metastatic lesions utilizing multi-omic analyses of serial biopsies and its implication for effective personalized treatment.

SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning.

Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin (H&E)-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that estimate the underlying distribution of the tumor cell marker pan-cytokeratin (panCK). To build a dataset suitable for learning this task, we developed a serial staining protocol which allows IF and H&E images from the same tissue to be spatially registered. We show that deep learning-extracted morphological feature representations of histological images can guide representative sample selection, which improved SHIFT generalizability in a small but heterogenous set of human pancreatic cancer samples. With validation in larger cohorts, SHIFT could serve as an efficient preliminary, auxiliary, or substitute for panCK IF by delivering virtual panCK IF images for a fraction of the cost and in a fraction of the time required by traditional IF.

DNA Methylation and Somatic Mutations Converge on the Cell Cycle and Define Similar Evolutionary Histories in Brain Tumors.

The evolutionary history of tumor cell populations can be reconstructed from patterns of genetic alterations. In contrast to stable genetic events, epigenetic states are reversible and sensitive to the microenvironment, prompting the question whether epigenetic information can similarly be used to discover tumor phylogeny. We examined the spatial and temporal dynamics of DNA methylation in a cohort of low-grade gliomas and their patient-matched recurrences. Genes transcriptionally upregulated through promoter hypomethylation during malignant progression to high-grade glioblastoma were enriched in cell cycle function, evolving in parallel with genetic alterations that deregulate the G1/S cell cycle checkpoint. Moreover, phyloepigenetic relationships robustly recapitulated phylogenetic patterns inferred from somatic mutations. These findings highlight widespread co-dependency of genetic and epigenetic events throughout brain tumor evolution.

Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma.

Tumor recurrence is a leading cause of cancer mortality. Therapies for recurrent disease may fail, at least in part, because the genomic alterations driving the growth of recurrences are distinct from those in the initial tumor. To explore this hypothesis, we sequenced the exomes of 23 initial low-grade gliomas and recurrent tumors resected from the same patients. In 43% of cases, at least half of the mutations in the initial tumor were undetected at recurrence, including driver mutations in TP53, ATRX, SMARCA4, and BRAF; this suggests that recurrent tumors are often seeded by cells derived from the initial tumor at a very early stage of their evolution. Notably, tumors from 6 of 10 patients treated with the chemotherapeutic drug temozolomide (TMZ) followed an alternative evolutionary path to high-grade glioma. At recurrence, these tumors were hypermutated and harbored driver mutations in the RB (retinoblastoma) and Akt-mTOR (mammalian target of rapamycin) pathways that bore the signature of TMZ-induced mutagenesis.

Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications.

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression.

Genome-scale mapping of DNase I sensitivity in vivo using tiling DNA microarrays.

Localized accessibility of critical DNA sequences to the regulatory machinery is a key requirement for regulation of human genes. Here we describe a high-resolution, genome-scale approach for quantifying chromatin accessibility by measuring DNase I sensitivity as a continuous function of genome position using tiling DNA microarrays (DNase-array). We demonstrate this approach across 1% ( approximately 30 Mb) of the human genome, wherein we localized 2,690 classical DNase I hypersensitive sites with high sensitivity and specificity, and also mapped larger-scale patterns of chromatin architecture. DNase I hypersensitive sites exhibit marked aggregation around transcriptional start sites (TSSs), though the majority mark nonpromoter functional elements. We also developed a computational approach for visualizing higher-order features of chromatin structure. This revealed that human chromatin organization is dominated by large (100-500 kb) 'superclusters' of DNase I hypersensitive sites, which encompass both gene-rich and gene-poor regions. DNase-array is a powerful and straightforward approach for systematic exposition of the cis-regulatory architecture of complex genomes.